AWS DeepLens Sample Projects Overview

To get started with AWS DeepLens, use the sample project templates.
AWS DeepLens sample projects are projects where the model is pre-trained so that all
you have
to do is create the project, import the model, deploy the project, and run the project.
Other sections in this guide teach you to extend a sample project's functionality
so that
it performs a specified task in response to an event, and train a sample project to
do
something different than the original sample.

Artistic Style Transfer

This project transfers the style of an image, such as a painting, to an entire video
sequence captured by AWS DeepLens.

This project shows how a Convolutional Neural Network (CNN) can apply the style of a painting to
your surroundings as it's streamed with your AWS DeepLens device. The project uses
a
pretrained optimized model that is ready to be deployed to your AWS DeepLens device.
After
deploying it, you can watch the stylized video stream.

You can also use your own image. After fine tuning the model for the image, you can
watch as the CNN applies the image's style to your video stream.

Project model: deeplens-artistic-style-transfer

Project function: deeplens-artistic-style-transfer

Object Recognition

This project shows you how a deep learning model can detect and recognize objects
in a
room.

The project uses the Single
Shot MultiBox Detector (SSD) framework to detect objects with a pretrained
resnet_50 network. The network has been trained on the Pascal VOC dataset and is
capable of recognizing 20 different kinds of objects. The model takes the video stream
from your AWS DeepLens device as input and labels the objects that it identifies.
The project
uses a pretrained optimized model that is ready to be deployed to your AWS DeepLens
device.
After deploying it, you can watch your AWS DeepLens model recognize objects around
you.

Face Detection and Recognition

With this project, you use a face detection model and your AWS DeepLens device to
detect
the faces of people in a room.

The model takes the video stream from your AWS DeepLens device as input and marks
the images of faces that it detects. The project uses a pretrained optimized model
that
is ready to be deployed to your AWS DeepLens device.

Project model: deeplens-face-detection

Project function: deeplens-face-detection

Hot Dog Recognition

Inspired by a popular television show, this project classifies food as either a
hot dog or not a hot dog.

It uses a model based on the SqueezeNet deep neural network. The model takes the video stream from
your AWS DeepLens device as input, and labels images as a hot dog or not a hot dog.
The
project uses a pretrained, optimized model that is ready to be deployed to your AWS
DeepLens
device. After deploying the model, you can use the Live View feature to watch as
the model recognizes hot dogs .

Cat and Dog Recognition

This project shows how you can use deep learning to recognize a cat or a
dog.

It is based on a convolutional neural network (CNN) architecture and uses a
pretrained Resnet-152
topology to classify an image as a cat or a dog. The project uses a pretrained,
optimized model that is ready to be deployed to your AWS DeepLens device. After deploying
it, you can watch as AWS DeepLens uses the model to recognize your pets.

Project model: deeplens-cat-and-dog-recognition

Project function: deeplens-cat-and-dog-recognition

Action Recognition

This project recognizes more than 30 kinds of activities.

It uses the Apache MXNet framework to transfer learning from a SqueezeNet trained
with ImageNet to a new task. The network has been tuned on a subset of the
UCF101 dataset and is capable of recognizing more than 30 different
activities. The model takes the video stream from your AWS DeepLens device as input
and
labels the actions that it identifies. The project uses a pretrained, optimized
model that is ready to be deployed to your AWS DeepLens device.

After deploying the model,
you can watch your AWS DeepLens use the model to recognize 37 different activities,
such as
applying makeup, applying lipstick, participating in archery, playing basketball,
bench pressing, biking,
playing billiards, blowing drying your hair, blowing out candles, bowling, brushing
teeth,
cutting things in the kitchen, playing a drum, getting a haircut, hammering,
handstand walking, getting a head massage, horseback riding, hula hooping, juggling,
jumping rope, doing jumping jacks, doing lunges, using nunchucks, playing a cello,
playing a flute, playing a guitar, playing a piano, playing a sitar, playing a violin,
doing pushups, shaving, skiing, typing, walking a dog, writing on a board, and playing
with a yo-yo.

Project model: deeplens-action-recognition

Project function: deeplens-action-recognition

Head Pose Detection

This sample project uses a deep learning model generated with the TensorFlow framework
to accurately detect the orientation of a person’s head.

This project uses the ResNet-50
network architecture to detect the orientation of the head. The network has been trained
on the Prima HeadPose dataset, which comprises 2,790 images of the faces of 15
people, with variations of pan and tilt angles from -90 to +90 degrees. We categorized
these head pose angles to 9 head pose classes: down right, right, up right, down,
middle, up, down left, left, and up left.

To help you get started, we have provided a pretrained, optimized model ready to
deploy to your AWS DeepLens device . After deploying the model, you can watch AWS
DeepLens recognize
various head poses.

Bird Classification

This project makes prediction of the top 5 bird species from a static bird photo captured
by the AWS DeepLens camera.

This project uses the ResNet-18 neural network architecture to train the model with the CUB-200 dataset. The trained model can identify 200 different bird species. Because the number of
categories are large, the project outputs only the top 5 most probable inference results.
To reduce the background noise for improved precision, a cropped zone located at the
middle of the camera image is used for inference. You can view the cropped zone from
the project video streaming. By positioning the static bird photo in the box zone,
inference results are illustrated on top left of the project view.

Project model: deeplens-bird-detection

Project function: deeplens-bird-detection

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